Operations | Monitoring | ITSM | DevOps | Cloud

Several factors—including AI adoption, investor expectations, and the rise of a new generation of innovative upstart companies—have driven a renewed focus on efficiency in every industry. But organizations that attempt to improve operational efficiency and drive profits via layoffs and short-term cost-cutting often end up hurting the business in the long run.

There’s a problem in engineering culture that we don’t talk about enough: observability is an afterthought. It’s treated as tooling, not thinking. As a checkbox, not a habit. And that mindset gap creates real consequences: longer outages, frustrated teams and massive business costs. Atlassian’s Incident Management for High-Velocity Teams overview cites a 2014 study by Gartner, that the average cost of IT downtime is $5,600 per minute.

Temporal lets you orchestrate complex, reliable workflows, but when something breaks or slows down, the built-in dashboards only give you a list of events and some basic filters. You can see what happened and filter by attributes like workflow type or namespace, but you can't drill deeper. There's no way to jump straight from a metric spike to the exact trace or log line you care about.

Business process automation no longer sits on the sidelines. What was once an emerging technology is now the engine behind modern business operations. In fact, around 60% of companies already use automation tools in their workflows, according to Duke University. This is not just companies — developers are also contributing to this shift by adopting low-code, no-code, and digital process automation platforms. These new tools remove barriers that once slowed innovation.

As AI adoption increases, the race to real, durable AI value intensifies. Almost every organization that can use AI is using AI — but one of the most troubling trends I’ve observed revolves around talent. Right now, most organizations use AI to increase internal efficiencies — do quick research, write quick emails, start a new project at the 50% mark rather than the 0% mark, etc. But some executives I’ve talked to are taking a more aggressive approach.

Implementing observability in Angular applications presents unique challenges. Understanding how users experience your application and identifying performance bottlenecks requires specialized tools and approaches. This guide covers implementing OpenTelemetry in Angular applications, with practical code examples for instrumentation, data collection, and integration with observability backends.

Troubleshooting failed cron jobs without proper logging can be frustrating. Ubuntu cron logs record the execution of scheduled tasks, helping you identify what's working and what isn't. This guide covers what engineers need to know about Ubuntu cron logs – from finding them to analyzing their contents and setting up effective monitoring solutions.

Disclaimer: This post focuses on Debian-based and Fedora\/RHEL-based distributions and packaging. Everybody using a GNU/Linux distribution most likely knows that packages used by the given distribution are somehow signed and such signatures are somehow verified. Usually, this knowledge comes with the first requirement to import some key when an extra package repository is being added to the system (the standard repositories of a distribution use keys that are present and trusted by default).

Let’s be honest, most AI (Artificial Intelligence) tools today are relatively smart but not always super useful. You ask them to explain something, write an email, maybe generate some code, but the moment you want them to do something real, like schedule a meeting, file a bug report, or move stuff across your tools, they just stare blankly. That’s where MCP comes in. MCP stands for Model Context Protocol.